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Abstract Energy harvesting in natural environment has attracted a great deal of attention to generate stable and continuous electrical energy. In this work, we proposed an advanced pyroelectric energy harvesting system by using form-stable phase change material (PCM) composites. The PCM composite connected pyro-electrode generated electrical polarization due to the change of external environment. Polyethylene glycol (PEG) and 1-tetradecanol (1-TD) composites with different phase transition field induced the temperature difference during light-on/-off process. Poly(vinylidene difluoride) (PVDF) was utilized for pyroelectric energy harvesting. The PVDF based pyro-electrode was applied changing the conditions of solar light irradiation and heat air flow. The PCM composites controlled the temperature fluctuation effectively and generated stable output electrical voltage and current. Numerical simulation was carried out to provided in-depth insight into the underlying physics of the system. We envisage that the developed thermal energy harvesting system can pave a way towards high-throughput and sustainable energy harvesting.

Abstract Steady-state free and forced convective cooling of vertical, rectangular, 3 mm thick, 250 mm long fins, uniformly separated and protruding vertically upwards from a 250 mm × 190 mm isothermal horizontal base was investigated. For each combination of specified fin protrusion, horizontal forced air flow-rate in the direction along the fins and fin base temperature, the optimal fin spacing—corresponding to the maximum rate of heat loss—has been deduced. As the fin protrusion of the heat exchanger increases, this optimal value rises significantly in forced convection conditions, but declines slightly in the presence of free convection alone. The temperature distributions over the fins' surfaces were also studied, when the fin base was maintained at constant temperatures of either 40°C, 60°C or 80°C above that of the ambient environment. Large temperature depressions occurred near the leading edges and tips of the fins in forced convection, whereas much nearer isothermal temperature distributions were present under free convection conditions.

Abstract The field of energy harvesting holds the promise of making our buildings “smart” if effective energy sources can be developed for use in ambient indoor conditions. Photovoltaics (PV), especially in its thin flexible form for easy integration, become a prime candidate for the aim, if tailored for low-density artificial light. We designed a test system which enabled us to measure the performance of PV devices under compact fluorescent lamp (CFL) and light-emitting diode (LED) illumination at different illuminance levels and compared polycrystalline and amorphous silicon cells with our own flexible dye solar cells (DSCs). Whereas poly-Si cells, with 15% outdoor efficiency, delivered at 200 lux under CFL only 2.8 μW/cm2 power density (and an efficiency of 4.4%), a-Si specifically designed for indoors, gave 5.9 μW/cm2 and 9.2% efficiency under the same CFL conditions (and 7.5% efficiency under LED). However, we show that the customization of flexible DSCs, by simply formulating ad-hoc less-concentrated, more transparent electrolytes, enabled these devices to outperform all others, providing average power densities of 8.0 μW/cm2 and 12.4% efficiencies under 200 lux CFL (more than quadruple compared to those measured at 1 sun), and 6.6 μW/cm2 and 10% efficiency under 200 lux LED illumination.

Abstract Algae have been considered as a promising biodiesel feedstock. One of the major factors affecting large-scale algae technology application is poor wintering cultivation performance. In this study, an integrated approach is investigated combining freshwater microalgae Chlorella zofingiensis wintering cultivation in pilot-scale photobioreactors with artificial wastewater treatment. Mixotrophic culture with the addition of acetic acid (pH-regulation group) and autotrophic culture (control group) were designed, and the characteristics of algal growth, lipid and biodiesel production, and nitrogen and phosphate removal were examined. The results showed that, by using acetic acid three times per day to regulate pH at between 6.8 and 7.2, the total nitrogen (TN) and total phosphate (TP) removal could be increased from 45.2% to 73.5% and from 92.2% to 100%, respectively. Higher biomass productivity of 66.94 mg L−1 day−1 with specific growth rate of 0.260 day−1 was achieved in the pH-regulation group. The lipid content was much higher when using acetic acid to regulate pH, and the relative lipid productivity reached 37.48 mg L−1 day−1. The biodiesel yield in the pH-regulated group was 19.44% of dry weight, with 16–18 carbons as the most abundant composition for fatty acid methyl esters. The findings of the study prove that pH adjustment using acetic acid is efficient in cultivating C. zofingiensis in wastewater in winter for biodiesel production and nutrient reduction.

Feature-based machine learning models for capacity and internal resistance (IR) curve prediction have been researched extensively in literature due to their high accuracy and generalization power. Most such models work within the high frequency of data availability regime, e.g., voltage response recorded every 1–4 s. Outside premium fee cloud monitoring solutions, data may be recorded once every 3, 5 or 10 min. In this low-data regime, there are little to no models available. This literature gap is addressed here via a novel methodology, underpinned by strong mathematical guarantees, called ‘path signature’. This work presents a feature-based predictive model for capacity fade and IR rise curves from only constant-current (CC) discharge voltage corresponding to the first 100 cycles. Included is a comprehensive feature analysis for the model via a relevance, redundancy, and complementarity feature trade-off mechanism. The ability to predict from subsampled ‘CC voltage at discharge’ data is investigated using different time steps ranging from 4 s to 4 min. It was discovered that voltage measurements taken at the end of every 4 min are enough to generate features for curve prediction with End of Life (EOL) and its corresponding IR values predicted with a mean absolute percentage error (MAPE) of approximately 13.2% and 2.1%, respectively. Our model under higher frequency (4 s) produces an improved accuracy with EOL predicted with an MAPE of 10%. Full implementation code publicly available.

Abstract The pressure-loss coefficient for a duct junction of square cross-section was determined using computational fluid dynamics (CFD). The predicted junction pressure-loss coefficient for combining flows was generally in good agreement with experimental data from the literature. The junction pressure-loss coefficient was associated with the flow from the side branch to the duct carrying the total flow.

Abstract The intervention on the existing building envelope thermal insulation is the main and effective solution in order to achieve a significant reduction of the building stock energy needs. The infrared technique is the methodology of the energy diagnosis aimed to identify qualitatively the principal causes of energy losses: the presence of thermal bridges. Those weak parts of the building envelope in terms of heat transfer result not easy to treat with an energy efficiency intervention, while they are gaining importance in the buildings total energy dispersion, as the level of insulation of opaque and transparent materials is continuously increasing. It is generally possible to evaluate the energy dispersions through these zones with a deep knowledge of the materials and the geometry using a numerical method. Besides, authors proposed in the past a methodology to assess the flux passing through thermal bridges with an infrared image correctly framed. The analysis of surface temperatures of the undisturbed wall and of the zone with thermal bridge, allows to define the Incidence Factor of the thermal Bridge (Itb). This parameter is strongly affected by the thermographic image accuracy, therefore, this paper deals with the development and validation of an innovative mathematical algorithm to enhance the image resolution and the consequent accuracy of the energy losses assessment. An experimental campaign in a controlled environment (hot box apparatus) has been conducted on three typologies of thermal bridge, firstly performing the thermographic survey and then applying the enhancement algorithm to the infrared images in order to compare the Itb and the linear thermal transmittance ψ values. Results showed that the proposed methodology could bring to an accuracy improvement up to 2% of the total buildings envelope energy losses evaluated by quantitative infrared thermography. Moreover, the proposed algorithm allows the implementation of a further process applicable to the images, in order to extract the physical boundaries of the hidden materials causing the thermal bridge, so revealing itself as a useful tool to identify exactly the suitable points of intervention for the thermal bridge correction. The application of the imaging process on the quantitative infrared thermography is an innovative approach that makes more accurate the evaluation of the actual heat loss of highly insulating buildings and reaching a higher detail on the detection and treating of thermal bridges.

This study presents the clockspeed analysis of a peer group comprising six major integrated US energy companies with substantial US interstate natural gas pipeline business activities: El Paso, Williams, NiSource, Kinder Morgan, MidAmerican and CMS Energy. For this peer group, the three clockspeed accelerators have been benchmarked at both corporate level and gas transmission business level, using time-series analysis and cross-sectional analysis over a 6-year period (2002–2007). The results are visualized in so-called clockspeed radargraphs. Overall corporate clockspeed winners – over the performance period studied – are: Williams, El Paso and Kinder Morgan; MidAmerican is a close follower. Corporate clockspeed laggards are: CMS Energy and NiSource. The peer group ranking for the natural gas transmission business segment shows similar clockspeed winners, but with different ranking in the following order: Kinder Morgan, MidAmerican and El Paso; Williams is a close follower. Clockspeed laggards for the natural gas transmission segments coincide with the corporate clockspeed laggards of the peer group: CMS Energy and NiSource (over the performance period studied); laggards of the past may become clockspeed leaders of the future if adjustments are made. Practical recommendations are formulated for achieving competitive clockspeed optimization in the US gas transmission industry as a whole. Recommendations for clockspeed acceleration at individual companies are also given. Although the US natural gas market is subject to specific regulations and its own geographical dynamics, this study also provides hints for improving the competitive clockspeed performance of gas transmission companies elsewhere, in other world regions.